Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Moses
20073.2k citationsPhilipp Koehn, Richard Zens et al.profile →
Citations per year, relative to Hieu Hoang Hieu Hoang (= 1×)
peers
Brooke Cowan
Countries citing papers authored by Hieu Hoang
Since
Specialization
Citations
This map shows the geographic impact of Hieu Hoang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hieu Hoang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hieu Hoang more than expected).
This network shows the impact of papers produced by Hieu Hoang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hieu Hoang. The network helps show where Hieu Hoang may publish in the future.
Co-authorship network of co-authors of Hieu Hoang
This figure shows the co-authorship network connecting the top 25 collaborators of Hieu Hoang.
A scholar is included among the top collaborators of Hieu Hoang based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Hieu Hoang. Hieu Hoang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hoang, Hieu, Nikolay Bogoychev, Lane Schwartz, & Marcin Junczys-Dowmunt. (2016). Fast, Scalable Phrase-Based SMT Decoding. Conference of the Association for Machine Translation in the Americas. 40–52.2 indexed citations
3.
Junczys-Dowmunt, Marcin, Tomasz Dwojak, & Hieu Hoang. (2016). Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions. 4.22 indexed citations
Durrani, Nadir, Alexander Fraser, Helmut Schmid, Hieu Hoang, & Philipp Koehn. (2013). Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?. Meeting of the Association for Computational Linguistics. 399–405.41 indexed citations
7.
Koehn, Philipp & Hieu Hoang. (2012). Open Source Statistical Machine Translation. Conference of the Association for Machine Translation in the Americas.1 indexed citations
8.
Heafield, Kenneth, et al.. (2011). Left language model state for syntactic machine translation.. Edinburgh Research Explorer (University of Edinburgh). 183–190.13 indexed citations
9.
Hoang, Hieu & Philipp Koehn. (2010). Improved Translation with Source Syntax Labels. Edinburgh Research Explorer (University of Edinburgh). 409–417.14 indexed citations
10.
Koehn, Philipp, Barry Haddow, Philip Williams, & Hieu Hoang. (2010). More Linguistic Annotation for Statistical Machine Translation. Edinburgh Research Explorer (University of Edinburgh). 115–120.15 indexed citations
11.
Hoang, Hieu, Philipp Koehn, & Adam Lopez. (2009). A Unified Framework for Phrase-Based, Hierarchical, and Syntax-Based Statistical Machine Translation. IWSLT. 152–159.36 indexed citations
Koehn, Philipp, Marcello Federico, Wade Shen, et al.. (2006). Open Source Toolkit for Statistical Machine Translation: Factored Translation Models and Lattice Decoding.6 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.